At low guidance weight (\(w=1\) ), EDGE achieves a level of diversity that closely matches that of the ground truth distribution while attaining state-of-the-art qualitative performance.
At high guidance weight (\(w=2\) ), EDGE produces dances of markedly higher fidelity, reflected by a significantly higher Elo rating, while trading off diversity.
These results reflect those of past studies, which show that sampling using guidance weights \(w > 1\)  significantly increases fidelity at the cost of diversity [1]}, [2]}.
This simple control over the diversity-fidelity tradeoff via the modulation of a single scalar parameter provides a powerful tool for downstream applications.
